{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experiment: \n",
    "\n",
    "Evaluate pruning by magnitude weighted by coactivations (more thorough evaluation), compare it to baseline (SET).\n",
    "\n",
    "#### Motivation.\n",
    "\n",
    "Check if results are consistently above baseline.\n",
    "\n",
    "#### Conclusion\n",
    "\n",
    "- No significant difference between both models\n",
    "- No support for early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import tabulate\n",
    "import pprint\n",
    "import click\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ray.tune.commands import *\n",
    "from nupic.research.frameworks.dynamic_sparse.common.browser import *\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.set_palette(\"colorblind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and check data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "exps = ['improved_magpruning_eval3', 'improved_magpruning_eval7', 'improved_magpruning_eval8']\n",
    "paths = [os.path.expanduser(\"~/nta/results/{}\".format(e)) for e in exps]\n",
    "df = load_many(paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Experiment Name</th>\n",
       "      <th>train_acc_max</th>\n",
       "      <th>train_acc_max_epoch</th>\n",
       "      <th>train_acc_min</th>\n",
       "      <th>train_acc_min_epoch</th>\n",
       "      <th>train_acc_median</th>\n",
       "      <th>train_acc_last</th>\n",
       "      <th>val_acc_max</th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th>val_acc_min</th>\n",
       "      <th>...</th>\n",
       "      <th>momentum</th>\n",
       "      <th>network</th>\n",
       "      <th>num_classes</th>\n",
       "      <th>on_perc</th>\n",
       "      <th>optim_alg</th>\n",
       "      <th>pruning_early_stop</th>\n",
       "      <th>test_noise</th>\n",
       "      <th>use_kwinners</th>\n",
       "      <th>weight_decay</th>\n",
       "      <th>weight_prune_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_on_perc=0.0</td>\n",
       "      <td>0.112367</td>\n",
       "      <td>30</td>\n",
       "      <td>0.106067</td>\n",
       "      <td>1</td>\n",
       "      <td>0.112367</td>\n",
       "      <td>0.112367</td>\n",
       "      <td>0.1135</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>MLPHeb</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_on_perc=0.005</td>\n",
       "      <td>0.160550</td>\n",
       "      <td>5</td>\n",
       "      <td>0.094450</td>\n",
       "      <td>4</td>\n",
       "      <td>0.112367</td>\n",
       "      <td>0.112367</td>\n",
       "      <td>0.1760</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0716</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>MLPHeb</td>\n",
       "      <td>10</td>\n",
       "      <td>0.005</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2_on_perc=0.01</td>\n",
       "      <td>0.692383</td>\n",
       "      <td>28</td>\n",
       "      <td>0.193100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.599850</td>\n",
       "      <td>0.648933</td>\n",
       "      <td>0.7023</td>\n",
       "      <td>28</td>\n",
       "      <td>0.2149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>MLPHeb</td>\n",
       "      <td>10</td>\n",
       "      <td>0.010</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3_on_perc=0.015</td>\n",
       "      <td>0.682750</td>\n",
       "      <td>3</td>\n",
       "      <td>0.394467</td>\n",
       "      <td>0</td>\n",
       "      <td>0.523317</td>\n",
       "      <td>0.523750</td>\n",
       "      <td>0.7275</td>\n",
       "      <td>19</td>\n",
       "      <td>0.4076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>MLPHeb</td>\n",
       "      <td>10</td>\n",
       "      <td>0.015</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4_on_perc=0.02</td>\n",
       "      <td>0.938533</td>\n",
       "      <td>35</td>\n",
       "      <td>0.580717</td>\n",
       "      <td>0</td>\n",
       "      <td>0.846358</td>\n",
       "      <td>0.833383</td>\n",
       "      <td>0.9446</td>\n",
       "      <td>34</td>\n",
       "      <td>0.6339</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>MLPHeb</td>\n",
       "      <td>10</td>\n",
       "      <td>0.020</td>\n",
       "      <td>SGD</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Experiment Name  train_acc_max  train_acc_max_epoch  train_acc_min  \\\n",
       "0    0_on_perc=0.0       0.112367                   30       0.106067   \n",
       "1  1_on_perc=0.005       0.160550                    5       0.094450   \n",
       "2   2_on_perc=0.01       0.692383                   28       0.193100   \n",
       "3  3_on_perc=0.015       0.682750                    3       0.394467   \n",
       "4   4_on_perc=0.02       0.938533                   35       0.580717   \n",
       "\n",
       "   train_acc_min_epoch  train_acc_median  train_acc_last  val_acc_max  \\\n",
       "0                    1          0.112367        0.112367       0.1135   \n",
       "1                    4          0.112367        0.112367       0.1760   \n",
       "2                    0          0.599850        0.648933       0.7023   \n",
       "3                    0          0.523317        0.523750       0.7275   \n",
       "4                    0          0.846358        0.833383       0.9446   \n",
       "\n",
       "   val_acc_max_epoch  val_acc_min  ...  momentum  network  num_classes  \\\n",
       "0                  2       0.0958  ...       0.9   MLPHeb           10   \n",
       "1                  5       0.0716  ...       0.9   MLPHeb           10   \n",
       "2                 28       0.2149  ...       0.9   MLPHeb           10   \n",
       "3                 19       0.4076  ...       0.9   MLPHeb           10   \n",
       "4                 34       0.6339  ...       0.9   MLPHeb           10   \n",
       "\n",
       "   on_perc optim_alg  pruning_early_stop  test_noise  use_kwinners  \\\n",
       "0    0.000       SGD                   2       False         False   \n",
       "1    0.005       SGD                   2       False         False   \n",
       "2    0.010       SGD                   2       False         False   \n",
       "3    0.015       SGD                   2       False         False   \n",
       "4    0.020       SGD                   2       False         False   \n",
       "\n",
       "  weight_decay weight_prune_perc  \n",
       "0       0.0001               0.3  \n",
       "1       0.0001               0.3  \n",
       "2       0.0001               0.3  \n",
       "3       0.0001               0.3  \n",
       "4       0.0001               0.3  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace hebbian prine\n",
    "df['hebbian_prune_perc'] = df['hebbian_prune_perc'].replace(np.nan, 0.0, regex=True)\n",
    "df['weight_prune_perc'] = df['weight_prune_perc'].replace(np.nan, 0.0, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Experiment Name', 'train_acc_max', 'train_acc_max_epoch',\n",
       "       'train_acc_min', 'train_acc_min_epoch', 'train_acc_median',\n",
       "       'train_acc_last', 'val_acc_max', 'val_acc_max_epoch', 'val_acc_min',\n",
       "       'val_acc_min_epoch', 'val_acc_median', 'val_acc_last', 'epochs',\n",
       "       'experiment_file_name', 'trial_time', 'mean_epoch_time', 'batch_norm',\n",
       "       'data_dir', 'dataset_name', 'debug_sparse', 'debug_weights', 'device',\n",
       "       'hebbian_grow', 'hebbian_prune_perc', 'hidden_sizes', 'input_size',\n",
       "       'learning_rate', 'lr_gamma', 'lr_milestones', 'lr_scheduler', 'model',\n",
       "       'momentum', 'network', 'num_classes', 'on_perc', 'optim_alg',\n",
       "       'pruning_early_stop', 'test_noise', 'use_kwinners', 'weight_decay',\n",
       "       'weight_prune_perc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(578, 42)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Experiment Name                                           1_on_perc=0.005\n",
       "train_acc_max                                                     0.16055\n",
       "train_acc_max_epoch                                                     5\n",
       "train_acc_min                                                     0.09445\n",
       "train_acc_min_epoch                                                     4\n",
       "train_acc_median                                                 0.112367\n",
       "train_acc_last                                                   0.112367\n",
       "val_acc_max                                                         0.176\n",
       "val_acc_max_epoch                                                       5\n",
       "val_acc_min                                                        0.0716\n",
       "val_acc_min_epoch                                                       4\n",
       "val_acc_median                                                     0.1135\n",
       "val_acc_last                                                       0.1135\n",
       "epochs                                                                100\n",
       "experiment_file_name    /Users/lsouza/nta/results/improved_magpruning_...\n",
       "trial_time                                                        48.6293\n",
       "mean_epoch_time                                                  0.486293\n",
       "batch_norm                                                           True\n",
       "data_dir                                        /home/ubuntu/nta/datasets\n",
       "dataset_name                                                        MNIST\n",
       "debug_sparse                                                         True\n",
       "debug_weights                                                        True\n",
       "device                                                               cuda\n",
       "hebbian_grow                                                        False\n",
       "hebbian_prune_perc                                                      0\n",
       "hidden_sizes                                                          100\n",
       "input_size                                                            784\n",
       "learning_rate                                                         0.1\n",
       "lr_gamma                                                              0.1\n",
       "lr_milestones                                                          60\n",
       "lr_scheduler                                                  MultiStepLR\n",
       "model                                                     DSNNWeightedMag\n",
       "momentum                                                              0.9\n",
       "network                                                            MLPHeb\n",
       "num_classes                                                            10\n",
       "on_perc                                                             0.005\n",
       "optim_alg                                                             SGD\n",
       "pruning_early_stop                                                      2\n",
       "test_noise                                                          False\n",
       "use_kwinners                                                        False\n",
       "weight_decay                                                       0.0001\n",
       "weight_prune_perc                                                     0.3\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model\n",
       "DSNNMixedHeb       184\n",
       "DSNNWeightedMag    210\n",
       "SparseModel        184\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('model')['model'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment Details"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# experiment configurations\n",
    "base_exp_config = dict(\n",
    "    device=\"cuda\",\n",
    "    # dataset related\n",
    "    dataset_name=\"MNIST\",\n",
    "    data_dir=os.path.expanduser(\"~/nta/datasets\"),\n",
    "    input_size=784,\n",
    "    num_classes=10,\n",
    "    # network related\n",
    "    network=\"MLPHeb\",\n",
    "    hidden_sizes=[100, 100, 100],\n",
    "    batch_norm=True,\n",
    "    use_kwinners=False,\n",
    "    # model related\n",
    "    model=tune.grid_search([\"DSNNWeightedMag\", \"DSNNMixedHeb\", \"SparseModel\"]),\n",
    "    on_perc=tune.grid_search(list(np.arange(0, 0.101, 0.005))),\n",
    "    optim_alg=\"SGD\",\n",
    "    momentum=0.9,\n",
    "    weight_decay=1e-4,\n",
    "    learning_rate=0.1,\n",
    "    lr_scheduler=\"MultiStepLR\",\n",
    "    lr_milestones=[30, 60, 90],\n",
    "    lr_gamma=0.1,\n",
    "    # sparse related\n",
    "    hebbian_prune_perc=None,\n",
    "    hebbian_grow=False,\n",
    "    weight_prune_perc=0.3,\n",
    "    pruning_early_stop=2,\n",
    "    # additional validation\n",
    "    test_noise=False,\n",
    "    # debugging\n",
    "    debug_weights=True,\n",
    "    debug_sparse=True,\n",
    ")\n",
    "\n",
    "# ray configurations\n",
    "tune_config = dict(\n",
    "    name=__file__.replace(\".py\", \"\") + \"_eval8\",\n",
    "    num_samples=7,\n",
    "    local_dir=os.path.expanduser(\"~/nta/results\"),\n",
    "    checkpoint_freq=0,\n",
    "    checkpoint_at_end=False,\n",
    "    stop={\"training_iteration\": 100},\n",
    "    resources_per_trial={\"cpu\": 1, \"gpu\": 0.20},\n",
    "    loggers=DEFAULT_LOGGERS,\n",
    "    verbose=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Did any  trials failed?\n",
    "df[df[\"epochs\"]<30][\"epochs\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(572, 42)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing failed or incomplete trials\n",
    "df_origin = df.copy()\n",
    "df = df_origin[df_origin[\"epochs\"]>=30]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131    11\n",
       "132    18\n",
       "133    10\n",
       "134    18\n",
       "135     9\n",
       "136     9\n",
       "Name: epochs, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which ones failed?\n",
    "# failed, or still ongoing?\n",
    "df_origin['failed'] = df_origin[\"epochs\"]<30\n",
    "df_origin[df_origin['failed']]['epochs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def mean_and_std(s):\n",
    "    return \"{:.3f} ± {:.3f}\".format(s.mean(), s.std())\n",
    "\n",
    "def round_mean(s):\n",
    "    return \"{:.0f}\".format(round(s.mean()))\n",
    "\n",
    "stats = ['min', 'max', 'mean', 'std']\n",
    "\n",
    "def agg(columns, filter=None, round=3):\n",
    "    if filter is None:\n",
    "        return (df.groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n",
    "    else:\n",
    "        return (df[filter].groupby(columns)\n",
    "             .agg({'val_acc_max_epoch': round_mean,\n",
    "                   'val_acc_max': stats,                \n",
    "                   'model': ['count']})).round(round)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Does improved weight pruning outperforms regular SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>val_acc_max_epoch</th>\n",
       "      <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
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       "      <th></th>\n",
       "      <th>round_mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>45</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.251</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>40</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.247</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>44</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.287</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                val_acc_max_epoch val_acc_max                      model\n",
       "                       round_mean         min    max   mean    std count\n",
       "model                                                                   \n",
       "DSNNMixedHeb                   45       0.114  0.983  0.874  0.251   181\n",
       "DSNNWeightedMag                40       0.114  0.982  0.873  0.247   210\n",
       "SparseModel                    44       0.114  0.977  0.802  0.287   181"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <td>0.014</td>\n",
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       "      <td>0.976</td>\n",
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       "      <td>0.012</td>\n",
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       "      <td>0.007</td>\n",
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       "      <td>0.961</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.006</td>\n",
       "      <td>28</td>\n",
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       "      <th>0.065</th>\n",
       "      <td>49</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.006</td>\n",
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       "      <td>0.003</td>\n",
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      ],
      "text/plain": [
       "        val_acc_max_epoch val_acc_max                      model\n",
       "               round_mean         min    max   mean    std count\n",
       "on_perc                                                         \n",
       "0.000                   0       0.114  0.114  0.114  0.000    28\n",
       "0.005                  18       0.114  0.546  0.195  0.111    28\n",
       "0.010                  28       0.175  0.898  0.535  0.221    28\n",
       "0.015                  30       0.385  0.937  0.748  0.173    28\n",
       "0.020                  45       0.433  0.958  0.868  0.139    28\n",
       "0.025                  52       0.722  0.966  0.915  0.071    28\n",
       "0.030                  57       0.793  0.971  0.941  0.049    28\n",
       "0.035                  52       0.935  0.975  0.961  0.014    28\n",
       "0.040                  56       0.945  0.976  0.965  0.012    28\n",
       "0.045                  61       0.951  0.977  0.968  0.010    28\n",
       "0.050                  54       0.956  0.978  0.970  0.008    28\n",
       "0.055                  45       0.960  0.979  0.972  0.007    28\n",
       "0.060                  48       0.961  0.980  0.974  0.006    28\n",
       "0.065                  49       0.965  0.980  0.975  0.006    26\n",
       "0.070                  52       0.967  0.981  0.976  0.005    26\n",
       "0.075                  48       0.969  0.982  0.977  0.005    26\n",
       "0.080                  39       0.970  0.982  0.977  0.004    26\n",
       "0.085                  41       0.970  0.982  0.978  0.004    26\n",
       "0.090                  41       0.971  0.982  0.978  0.004    26\n",
       "0.095                  41       0.973  0.983  0.979  0.004    26\n",
       "0.100                  45       0.972  0.982  0.979  0.003    26"
      ]
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "agg(['on_perc'])"
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.245</td>\n",
       "      <td>0.161</td>\n",
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       "      <th>DSNNWeightedMag</th>\n",
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       "      <td>0.203</td>\n",
       "      <td>0.084</td>\n",
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       "      <td>0.029</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">0.010</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
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       "      <td>0.360</td>\n",
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       "      <td>0.634</td>\n",
       "      <td>0.221</td>\n",
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       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
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       "      <td>0.856</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.111</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">0.015</th>\n",
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       "      <th>DSNNWeightedMag</th>\n",
       "      <td>26</td>\n",
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       "      <td>0.924</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.107</td>\n",
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       "      <th>SparseModel</th>\n",
       "      <td>37</td>\n",
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       "      <td>0.613</td>\n",
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       "      <td>0.068</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">0.020</th>\n",
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       "      <td>0.949</td>\n",
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       "      <th>DSNNWeightedMag</th>\n",
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       "      <td>0.702</td>\n",
       "      <td>0.142</td>\n",
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       "      <td>0.961</td>\n",
       "      <td>0.004</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>39</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.008</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>66</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.068</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.030</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>71</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.002</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>42</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.003</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>59</td>\n",
       "      <td>0.793</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.058</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.035</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>48</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.002</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>43</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.002</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>66</td>\n",
       "      <td>0.935</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.004</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.040</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>61</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.001</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>53</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.002</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>53</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.004</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.045</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>56</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.001</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>56</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>72</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.002</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.055</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>47</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.001</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>44</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>45</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.002</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.060</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>51</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.001</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>52</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>41</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.002</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.065</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>56</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>43</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>50</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.966</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.070</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>66</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>43</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>48</td>\n",
       "      <td>0.967</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.968</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.075</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>53</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>51</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>42</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.080</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>36</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>45</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>36</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.085</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>35</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>46</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>42</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.090</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>47</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>43</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>32</td>\n",
       "      <td>0.971</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.095</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>48</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.983</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>39</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>37</td>\n",
       "      <td>0.973</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">0.100</th>\n",
       "      <th>DSNNMixedHeb</th>\n",
       "      <td>51</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.001</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSNNWeightedMag</th>\n",
       "      <td>46</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.981</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SparseModel</th>\n",
       "      <td>39</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.002</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        val_acc_max_epoch val_acc_max                       \\\n",
       "                               round_mean         min    max   mean    std   \n",
       "on_perc model                                                                \n",
       "0.000   DSNNMixedHeb                    0       0.114  0.114  0.114  0.000   \n",
       "        DSNNWeightedMag                 0       0.114  0.114  0.114  0.000   \n",
       "        SparseModel                     0       0.114  0.114  0.114  0.000   \n",
       "0.005   DSNNMixedHeb                   16       0.114  0.546  0.245  0.161   \n",
       "        DSNNWeightedMag                30       0.114  0.362  0.203  0.084   \n",
       "        SparseModel                     5       0.114  0.183  0.134  0.029   \n",
       "0.010   DSNNMixedHeb                   35       0.360  0.898  0.634  0.221   \n",
       "        DSNNWeightedMag                22       0.528  0.856  0.661  0.111   \n",
       "        SparseModel                    28       0.175  0.398  0.295  0.083   \n",
       "0.015   DSNNMixedHeb                   27       0.819  0.937  0.878  0.050   \n",
       "        DSNNWeightedMag                26       0.653  0.924  0.829  0.107   \n",
       "        SparseModel                    37       0.385  0.613  0.528  0.068   \n",
       "0.020   DSNNMixedHeb                   46       0.936  0.958  0.949  0.008   \n",
       "        DSNNWeightedMag                29       0.929  0.952  0.942  0.007   \n",
       "        SparseModel                    62       0.433  0.848  0.702  0.142   \n",
       "0.025   DSNNMixedHeb                   51       0.955  0.966  0.961  0.004   \n",
       "        DSNNWeightedMag                39       0.934  0.962  0.951  0.008   \n",
       "        SparseModel                    66       0.722  0.923  0.828  0.068   \n",
       "0.030   DSNNMixedHeb                   71       0.964  0.971  0.968  0.002   \n",
       "        DSNNWeightedMag                42       0.960  0.968  0.964  0.003   \n",
       "        SparseModel                    59       0.793  0.940  0.888  0.058   \n",
       "0.035   DSNNMixedHeb                   48       0.969  0.975  0.972  0.002   \n",
       "        DSNNWeightedMag                43       0.966  0.972  0.969  0.002   \n",
       "        SparseModel                    66       0.935  0.945  0.941  0.004   \n",
       "0.040   DSNNMixedHeb                   61       0.972  0.976  0.974  0.001   \n",
       "        DSNNWeightedMag                53       0.969  0.975  0.972  0.002   \n",
       "        SparseModel                    53       0.945  0.954  0.949  0.004   \n",
       "0.045   DSNNMixedHeb                   56       0.974  0.977  0.976  0.001   \n",
       "        DSNNWeightedMag                56       0.971  0.975  0.973  0.001   \n",
       "        SparseModel                    72       0.951  0.956  0.954  0.002   \n",
       "...                                   ...         ...    ...    ...    ...   \n",
       "0.055   DSNNMixedHeb                   47       0.976  0.979  0.978  0.001   \n",
       "        DSNNWeightedMag                44       0.974  0.977  0.976  0.001   \n",
       "        SparseModel                    45       0.960  0.965  0.963  0.002   \n",
       "0.060   DSNNMixedHeb                   51       0.976  0.980  0.979  0.001   \n",
       "        DSNNWeightedMag                52       0.976  0.978  0.977  0.001   \n",
       "        SparseModel                    41       0.961  0.968  0.965  0.002   \n",
       "0.065   DSNNMixedHeb                   56       0.978  0.980  0.979  0.001   \n",
       "        DSNNWeightedMag                43       0.976  0.979  0.978  0.001   \n",
       "        SparseModel                    50       0.965  0.968  0.966  0.001   \n",
       "0.070   DSNNMixedHeb                   66       0.978  0.981  0.980  0.001   \n",
       "        DSNNWeightedMag                43       0.977  0.980  0.978  0.001   \n",
       "        SparseModel                    48       0.967  0.970  0.968  0.001   \n",
       "0.075   DSNNMixedHeb                   53       0.979  0.982  0.980  0.001   \n",
       "        DSNNWeightedMag                51       0.978  0.981  0.979  0.001   \n",
       "        SparseModel                    42       0.969  0.971  0.970  0.001   \n",
       "0.080   DSNNMixedHeb                   36       0.980  0.982  0.981  0.001   \n",
       "        DSNNWeightedMag                45       0.978  0.981  0.979  0.001   \n",
       "        SparseModel                    36       0.970  0.974  0.972  0.001   \n",
       "0.085   DSNNMixedHeb                   35       0.979  0.982  0.981  0.001   \n",
       "        DSNNWeightedMag                46       0.978  0.981  0.980  0.001   \n",
       "        SparseModel                    42       0.970  0.974  0.972  0.001   \n",
       "0.090   DSNNMixedHeb                   47       0.978  0.982  0.981  0.001   \n",
       "        DSNNWeightedMag                43       0.979  0.982  0.981  0.001   \n",
       "        SparseModel                    32       0.971  0.974  0.973  0.001   \n",
       "0.095   DSNNMixedHeb                   48       0.979  0.983  0.981  0.001   \n",
       "        DSNNWeightedMag                39       0.978  0.982  0.980  0.001   \n",
       "        SparseModel                    37       0.973  0.975  0.974  0.001   \n",
       "0.100   DSNNMixedHeb                   51       0.980  0.982  0.981  0.001   \n",
       "        DSNNWeightedMag                46       0.980  0.982  0.981  0.001   \n",
       "        SparseModel                    39       0.972  0.977  0.974  0.002   \n",
       "\n",
       "                        model  \n",
       "                        count  \n",
       "on_perc model                  \n",
       "0.000   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.005   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.010   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.015   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.020   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.025   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.030   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.035   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.040   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.045   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "...                       ...  \n",
       "0.055   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.060   DSNNMixedHeb        9  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         9  \n",
       "0.065   DSNNMixedHeb        8  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         8  \n",
       "0.070   DSNNMixedHeb        8  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         8  \n",
       "0.075   DSNNMixedHeb        8  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         8  \n",
       "0.080   DSNNMixedHeb        8  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         8  \n",
       "0.085   DSNNMixedHeb        8  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         8  \n",
       "0.090   DSNNMixedHeb        8  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         8  \n",
       "0.095   DSNNMixedHeb        8  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         8  \n",
       "0.100   DSNNMixedHeb        8  \n",
       "        DSNNWeightedMag    10  \n",
       "        SparseModel         8  \n",
       "\n",
       "[63 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg(['on_perc', 'model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# translate model names\n",
    "rcParams['figure.figsize'] = 16, 8\n",
    "d = {\n",
    "    'DSNNWeightedMag': 'DSNN',\n",
    "    'DSNNMixedHeb': 'SET',\n",
    "    'SparseModel': 'Static',        \n",
    "}\n",
    "df_plot = df.copy()\n",
    "df_plot['model'] = df_plot['model'].apply(lambda x: d[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2469ca58>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 947
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.scatterplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')\n",
    "sns.lineplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121be2f60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 944
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 16, 8\n",
    "filter = df_plot['model'] != 'Static'\n",
    "sns.lineplot(data=df_plot[filter], x='on_perc', y='val_acc_max_epoch', hue='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24446898>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 484,
       "width": 947
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df_plot, x='on_perc', y='val_acc_last', hue='model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
